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Título: | Two-Leak Isolation in Water Distribution Networks Based on k-NN and Linear Discriminant Classifiers |
Autor: | Rodríguez-Argote, Carlos Andrés; Begovich-Mendoza, Ofelia; Navarro-Díaz, Adrián; Santos-Ruiz, Ildeberto CSIC ORCID; Puig, Vicenç CSIC ORCID; Delgado-Aguiñaga, Jorge A. | Palabras clave: | Discriminant analysis k-NN classification Leak diagnosis Machine learning Water distribution network |
Fecha de publicación: | 1-sep-2023 | Editor: | Molecular Diversity Preservation International | Citación: | Water 15(17): 3090 (2023) | Resumen: | In this paper, the two-simultaneous-leak isolation problem in water distribution networks is addressed. This methodology relies on optimal sensor placement together with a leak location strategy using two well-known classifiers: k-NN and discriminant analysis. First, zone segmentation of the water distribution network is proposed, aiming to reduce the computational cost that involves all possible combinations of two-leak scenarios. Each zone is composed of at least two consecutive nodes, which means that the number of zones is at most half the number of nodes. With this segmentation, the leak identification task is to locate the zones where the pair of leaks are occurring. To quantify the uncertainty degree, a relaxation node criterion is used. The simulation results evidenced that the outcomes are accurate in most cases by using one-relaxation-node and two-relaxation-node criteria. | Versión del editor: | https://doi.org/10.3390/w15173090 | URI: | http://hdl.handle.net/10261/351227 | DOI: | 10.3390/w15173090 | E-ISSN: | 2073-4441 |
Aparece en las colecciones: | (IRII) Artículos |
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